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118 | Adoption and Implementation of ERP
Journal of Management and Research (JMR) Volume 6(1): 2019
Adoption and Implementation of Enterprise Resource
Planning (ERP): An Empirical Study
Mohammad Sarwar Alam1
Md. Aftab Uddin2
Abstract
The study is an attempt to unearth the current state of Enterprise
Resource Planning (ERP) adoption and implementation in both
manufacturing and service firms. Drawing on the conceptualization of
multiple theories based in technology acceptance and innovation
diffusion model, this study examines the above objectives with a
particular reference to the Unified Theory of Acceptance and Use of
Technology (UTAUT). Following the deductive reasoning approach, we
applied a self-administered questionnaire survey and used 235 replies
from 255 collected responses, leaving the data affected with missing and
un-matched responses. The result is applied using the structural
equation modeling via SmartPLS 2, a second generation regression
model, for testing hypothesized relationships. Findings reveal that all the
hypothesized influences are found significantly linked through the
explanatory variables with the endogenous variables at different levels
of significance, except the impact of effort efficiency and resistance to
change. Policy implications are also proposed for the full adoption and
utilization of ERP to achieve sustainable development goals.
Furthermore, we recommend future researchers to focus on action
research or experimental data for preventing the generalizability of the
observed results.
1Department of Management, University of Chittagong, Bangladesh 2Department of Human Resource Management, University of Chittagong, Bangladesh
Correspondence concerning this article should be addressed to Md. Aftab Uddin,
Department of Human Resource Management, University of Chittagong, Bangladesh. E-
mail: [email protected]
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Keywords: ERP, ERP adoption, ERP implementation, UTAUT model.
1. Introduction
Amid today’s globalized and competitive market, realizing sustainable
competitive advantage is the key to reap organizational success (Porter,
1991). In this newly formed business horizon, it is very challenging and
even close to impossible to successfully compete and outrun the key market
players without developing an integrated and flexible supply chain
management (Lambert & Cooper, 2000). Hence, new organizations are
making significant investments in sophisticated Information Systems (IS)
such as Enterprise Resource Planning (ERP) systems to cope with the
dynamic environment of business. A more in-depth look into the ERP
system represents a few core processes such as accounting, procurement,
material and inventory management, project management, manufacturing
operations, finance to operate and to deliver the final products and
information to customers (Reitsma & Hilletofth, 2018). ERP systems tie
together plenty of core business processes for enabling the flow of data and
information between them (Awa, Uko, & Ukoha, 2017). It reduces the
redundancy by centralizing the data from multiple sources, which thereby
eliminates the data duplication cost and leaking (Ali & Miller, 2017;
ORACLE, 2018). Until recently, ERP vendors were rendering customized
package services to firms irrespective of their size, growth, and business
lines (Everdingen, Hillegersberg, & Waarts, 2000).
Globally, the use of ERP systems has increased significantly.
Interestingly, almost 28.5% of the global ERP market has been occupied by
the top 10 vendors with a growth rate of 1.4% to reach $82.2 billion market
value of the subscription, maintenance, and license (Pang,
2017). Organizations in developed and developing countries are pursuing
ERP to stand out globally for facilitating their growth beyond their previous
in-house systems (Huang & Palvia, 2001; Markus & Tanis, 2000). To keep
them ahead in the competitive arena, each organization is making a better
use of ERP. Thus, it is strategically critical for all firms, not only for their
adoption and implementation of ERM usages but also to prepare their users
to reap the most tangible and intangible services it provides (Chang,
Cheung, Cheng, & Yeung, 2008).
Despite the rampant diffusion of ERP technologies in advanced nations,
their relative adoption and implementation in developing countries and least
developed countries is relatively scarce (Asamoah & Andoh, 2018; Singh,
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2007). Notably, the relative contribution of Asian nations is found only
seven percent as compared to the global ERP implementation (Costa,
Ferreira, Bento, & Aparicio, 2016). Moreover, more than half of ERP
investment ended in acute failure globally (Ali & Miller, 2017; Darr, 2015;
Rajan & Baral, 2015). Nobody can deny the inevitability of investment in
the technical aspect. However, there are several behavioral issues or factors
stimulating success in the adoption and implementation of ERP (Costa et
al., 2016; Rajan & Baral, 2015; United Nations Organization, 2016).
Nevertheless, the growth in the adoption of web enabled applications is
evident in recent years (Costa et al., 2016; Star, 2018); however, the relative
growth during corporate transformation through the use of ERP system is
not well-documented.
It is crystal clear from the previous literature that the rate of ERP
adoption in developing countries has not been studied adequately (Huang
& Palvia, 2001). Only a limited number of studies are witnessed to identify
the antecedents influencing ERP software adoption in developing countries
(Rajan & Baral, 2015). Besides, to ensure the optimization of ERP adoption
through effective and efficient operationalization apart from technical
specifications, organizations must sort out the behavioral factors that make
the adoption and implementation of ERP complex (Awa et al., 2017; Nandi
& Vakkayil, 2018). Essentially, it is critical to explore and examine the
behavioral factors impacting the successful adoption and implementation of
ERP in the Bangladeshi context. Henceforth, the aim of this research is to
explore the rate of ERP adoption and the factors which influence its
successful implementation in different industries in Bangladesh. We will
investigate the relationship between a number of factors including
performance expectancy, effort expectancy, social influence, facilitating
conditions, resistance to change, perceived credibility, and actual usage with
behavioral intention and how these variables influence the individuals’ use
and implementation of the system.
2. Literature Review
2.1 Enterprise Resource Planning
ERP is an integrated package software that combines entire business
processes into a single information technology architecture for providing a
holistic view of the whole business (Klaus, Rosemann, & Gable, 2000, p.
141). ERP has a modular structure and provides integrated information flow
across each function of business using an integrated network across
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functional areas in a database (Davenport, 1998). Since the beginning of
ERP in the mid-1990s, it has been used to outline and organize business
processes across all organizational departments (Krotov, Boukhonine, &
Ives, 2011; Rouhani & Mehri, 2018). The integrated system ensures the
same pace of performance across various levels in an organization
(McAfee, 2009). The adoption of ERP requires enormous investments in
software to customize it according to the end users requirements (Doom,
Milis, Poelmans, & Bloemen, 2010). The uniqueness of ERP technologies
is that (i) it integrates all business functions and processes; (ii) restricts the
entry of the same data from different sources; (iii) upgrades technology; (iv)
enables the systems’ portability and adaptability; and (v) applies the best
practices (Saatçıoğlu, 2009).
The failure of an organization in implementing ERP will result in losing
productivity and competitive advantage at all levels of value creating
entities (Addo & Helo, 2011; Rouhani & Mehri, 2018). The contribution of
ERP is myriad since it emerges from multiple fields and remains
multidisciplinary (Moon, 2007). The study of Esteves and Bohorquez
(2007) showed that success in getting an expected result from an ERP
project vastly depends on its implementation in addition to its adoption.
Therefore, successful implementation requires sweeping changes in entire
systems, processes, and other social dimensions through collaborative
endeavor and attitude toward the perceived outcome (Kwahk & Kim,
2008). Figure 1 demonstrates the perceived influence of numerous
antecedents on the intention to use and the actual use.
2.1.1 Performance Expectancy
Performance expectancy in the UTAUT model is the most critical
determinant which explains behavioral intention very well. An individual
believes that using this particular ERP system will result in meaningful
performance (Venkatesh, Morris, Davis, & Davis, 2003, p. 447). It shows
the measurements of the user of a system manifesting whether the system is
advantageous, performance enhancer, user friendly or not.
2.1.2 Effort Expectancy
Effort expectancy refers to the degree of ease associated with the use of ERP
technology (Venkatesh et al., 2003, p. 450). This construct is developed
from the ideation of the perceived ease in use and the complexity involved
in its usage behavior. Whether an individual inclines to use a new
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technology is accurately reflected by effort expectancy. According to
Aggelidis and Chatzoglou (2009), effort expectancy is an antecedent of
users’ intention to use ERP.
2.1.3 Social Influence
Social influence refers to the expectation of the society from an individual
keeping in view how important others expect him/her to use the new ERP
technology (Venkatesh et al., 2003, p. 451). This construct is derived from
subjective norm, image, and social factors. Surrounding environs of a
person, which can shape human thoughts and perception, are the
determinants of the intention to use new technology (Qi Dong, 2009). Thus,
social influence is a significant predictor of how an individual intends to
adopt a new technology, especially when people are less involved with it.
According to Lu, Yao, and Yu (2005), at the early stage of technological
adoption, social influence reserves a revealing impact of users’ behavioral
intention to use.
2.2 Facilitating Conditions
To adopt new technology, it is necessary that organizational and technical
infrastructure exists for supporting any new adoption of that novel
technology. Venkatesh et al. (2003, p. 453) posited that facilitating
conditions, such as perceived compatibility and technical and infrastructural
support are a must to support the use of a new system. Yi, Jackson, Park,
and Probst (2006) reported the direct influence of facilitating conditions on
the use of technology. People always seek assistance if technology is new
to them. Therefore, unavailability of supporting circumstances in an
organization may create ambiguity or negligence while adopting a novel
technology (Qi Dong, 2009; Venkatesh, Thong, Chan, Hu, & Brown,
2011).
2.3 Resistance to Change
One of the most common phenomena in individual, group and
organizational behavior is the resistance to change (Audia & Brion, 2007).
Resistance to change is driven from the individual’s perception of potential
threat or powerlessness (Gupta, Misra, Kock, & Roubaud, 2018; Hasan,
Ebrahim, Mahmood, & Rahmanm, 2018). Resistance to change is viewed
as one of the top reasons why any endeavor for change fails to see hope
(Huy, 1999). Therefore, in case of any organizational change, such as
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acceptance of any new technology or adoption of an ERP system, people
exhibit anxiety in accepting it or even try to resist it (Rouhani & Mehri,
2018).
2.4 Perceived Credibility
Perceived credibility deals with the trust and beliefs of end user while
adopting a new system. It refers to the degree of belief and trust of the end
user in the information being received (Meyer, 1988). The collected data
and information must be convincing and credible enough to build a positive
attitude in the end user to induce him/her to select the technology. Thus,
trust and credibility are critical aspects of regulating an individual’s
behavior. Employees and the management use credible information while
transcending their attitude toward ERP, which in turn influences their
behavioral intention (Panigrahi, Zainuddin, & Azizan, 2014).
2.5 Intension to Use
Intention to use is defined as “the degree of evaluative influence that an
individual relates with the target system in his or her job” (Venkatesh &
Morris, 2000). It refers to the positive evaluation of user position to adopt a
new technology. Without the growing behavioral intention to use a
particular technology, it must be an utter surprise to experience the actual
usage of it. Thus, extant literature documented the direct influence of
intention to use a system on actual usage behavior (Carlsson, Carlsson,
Hyvonen, Puhakainen, & Walden, 2006). Henceforth, the practical use of a
given technology entirely depends on the employee's intention to use it.
3. Theoretical Framework
End users’ acceptance of technology and information systems has received
constant attention from academics and professionals since its inauguration
(Bhatiasevi, 2016). Since then, numerous studies have been documented
about the identification of the factors responsible for the adoption
(resistance) and implementation of a new technology. Studies have
observed various theoretical underpinnings explaining the acceptance of the
new technology. Technology acceptance model among others, such as the
theory of reasoned action, the theory planned behavior, social cognitive
theory, the extended technology acceptance model, innovation diffusion
theory, and the model of perceived credibility theory are popularly used
(Khanam, Uddin, & Mahfuz, 2015). However, studies have also attested
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that certain concerns restrict the broader acceptability of these theories
(Bhatiasevi, 2016). Firstly, these different theories demonstrate similar
terminologies. Secondly, studies show that behavioral adoption is a
complex process which is not covered in its entirety by any of these theories.
Finally, the absence of any comprehensive model triggers the researchers to
use the fragmented model or constructs while ignoring other vital constructs
of interest.
To guard those concerns, Venkatesh et al. (2003) stressed on reviewing
and synthesizing available literature to form a unified model. Venkatesh et
al. (2003) advocated the Unified Theory of Acceptance and Use of
Technology (UTAUT), integrating the previous fragmented theories (eight)
and empirical findings (Bhatiasevi, 2016). The UTAUT model has been put
forth and applied in this study to find out the adoption and implementation
of ERP in the industrial settings of Bangladesh (Venkatesh et al., 2011). The
UTAUT model comprises five distinct constructs including effort
expectancy, performance expectancy, facilitating conditions, social
influence, and behavioral intention, which are the direct antecedents of
usage behavior (Venkatesh et al., 2003; Venkatesh, Thong, & Xu, 2012).
Building on the essence of the basic UTAUT model, the current study
adopted all the five constructs mentioned above. Besides, the adoption of
ERP technology in an industrial context has to deal with other contextual
difficulties because of the digital divide among people who are intimidated
by technological change and the loss of credibility threatened by any given
change (Rajan & Baral, 2015). Resistance to change among the end users is
due to the shift from the previously held status quo such as no ERP usage,
to ERP adoption and implementation (Laumer, Maier, Eckhardt, & Weitzel,
2016) and its perceived credibility from a given technological change
warrants remarkable influences on the adoption of ERP analytics.
Henceforth, with little extension in the UTAUT model, it is essential to
explore the impacts of underlying antecedents on explanatory variables, that
is, intention to use and actual use of ERP.
3.1 Hypothesis Development
Performance expectancy is assumed to be a strong predictor of exhibiting
user intention. Similarly, effort expectancy has shown the relation of
behavioral intention to usage (Davis, 1989). Recent studies on this issue
witnessed mixed results. However, the studied effects were observed by
global scholars in the management information system arena as one of the
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critical predictors of the user’s behavioral intention (Petter, DeLone, &
McLean, 2008). They observed a stronger association between effort
expectancy and behavioral intention to use (Youngberg, Olsen, & Hauser,
2009). Extant studies also noticed similar findings of performance
expectancy and effort expectancy to examine the behavioral intention to use
ERP (Rajan & Baral, 2015; Sternad & Bobek, 2013). In summary, it
suffices to believe on the basis of the empirical and theoretical evidence that
performance expectancy and effort expectancy can predict the user’s
behavioral intention about the actual use of the ERP system.
H1. Performance expectancy of ERP affects users’ behavioral intention.
H2. Effort expectancy of ERP influences users’ behavioral intention.
Social influence is the strongest predictor when we study users’
intention towards the adoption of new technology (Lu et al., 2005).
Although there is a limited number of studies available on the influence of
user’s intention on ERP adoption, the need of a solid theoretical basis for
predicting this relation cannot be overlooked (Venkatesh & Davis, 2000).
In the parlance of ERP studies, this impact of the essential others on the
intention to use ERP is theoretically relevant. Sun, Bhattacherjee, and Ma
(2009) suggested that social influence has an impact on behavioral intention
to use the ERP system and Calisir, Gumussoy, and Bayram (2009) also
identified a significant co-relationship between subjective norms driven by
social influence and users’ behavioral intention to use ERP in business
organizations (Venkatesh et al., 2003; Wagaw, 2017). Based on these
arguments, we set forth the following hypothesis,
H3. Social influence has an impact on users’ behavioral intention.
Facilitating conditions demonstrate the prevalence of perceived
organizational and technical infrastructure to support the use of the system
(Venkatesh et al., 2003). Yi et al. (2006) showed that facilitating conditions
impact the user’s behavioral intention to use a particular technology.
Infrastructural support plays a vital role in adopting technology and systems
use (Bhattacherjee & Hikmet, 2008). A study by Boontarig, Chutimaskul,
Chongsuphajaisiddhi, and Papasratorn (2012) suggested that facilitating
conditions positively influence the behavioral intention and usage behavior
of any technology. Therefore, we can reveal the following hypothesis in
light of the above discussion,
H4. Facilitating conditions have a significant effect on users’ behavioral
intention.
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Perceived credibility and resistance to change have been identified as
additional variables which influence the adoption of an ERP system.
Resistance to change reduces the user’s effort for the adoption of new
technology (Guo, Sun, Wang, Peng, & Yan, 2013). It is a general perception
that people are afraid of new things and naturally decline to change (Smither
& Braun, 1994). Henceforth, we can say that this fear of people leads them
to experience anxiety while adopting a new technology (Hasan et al., 2018).
Being a new technology in developing countries’ context, the adoption of
ERP triggers a significant resistance from users. Essentially, it turns out to
be a tough task to make the users ready to use ERP. Hence, we propose the
following hypothesis,
H5. Resistance to change has adverse effects on users’ behavioral
intention.
On the other hand, perceived credibility is another critical variable in
developing countries’ which strongly affects behavioral intention. Trust and
credibility are the crucial factors which influence usage behavior.
According to Yagci, Biswas, and Dutta (2009), “the positive evaluative
beliefs on credibility subsidize significantly to individual’s attitude.” The
establishment of trust in new technology is very significant because
confidence in new technology will let them believe that new technology will
guarantee them higher efficiency and better living, replacing the old.
Therefore, this belief leads to positive attitudes towards behavioral
intentions of using ERP. Accordingly, we developed the hypothesis given
below,
H6. Perceived credibility has a positive influence on users’ behavioral
intention.
Finally, several studies have documented the pertinent association
between behavioral intention and actual usage, which indicates that
behavioral intention is a significant predictor and one of the crucial
determinants of actual usage (Sheppard, Hartwick, & Warshaw, 1988;
Venkatesh & Morris, 2000). Venkatesh et al. (2003) examined and
demonstrated that the user’s behavioral intention strongly impacts actual
usage. Furthermore, Legris, Ingham, and Collerette (2003) in a meta-
analysis, found that the relation between behavioral intention and actual
usage was found positive almost in all studies. In other reviews in
information systems field, behavioral intention is highlighted as the
strongest predictor of actual ERP use (Sternad & Bobek, 2013; Youngberg
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et al., 2009). Henceforth, in case of adopting an ERP system one’s
behavioral intention will surely and positively influence actual usage
behavior. So, the following hypothesis is proposed,
H7. Behavioral intention has an impact on actual usage.
4.Research Methods
4.1 Data Collection Procedure
A total of 400 self-administered questionnaires were distributed among
employees working at different levels in a wide range of industries operating
in Bangladesh. Self-administered questionnaire survey technique was
chosen because it yields maximum response via email, physical visits,
postal services and saves the cost and time consumed in a survey (Zikmund,
Babin, Carr, & Griffin, 2010). We delivered the survey instruments to
informants through a personal visit and also via email when respondents
were unavailable during physical visits. We visited the respondents’ facility
many times to distribute, remind, and collect data. To prevent response and
social desirability bias, we assured them that their identities would be kept
private, and this research will only report on the general industrial scenarios.
This assurance drove them to respond accurately while keeping their
identities secret and saving their faces (MacKenzie & Podsakoff, 2012;
Podsakoff, MacKenzie, & Podsakoff, 2012). A total of 255 usable
Performance Expectancy
Effort Expectancy
Perceived Credibility
Actual Usage
Intention to Use
Facilitating Condition
Social Influence
Resistance to Change
Figure 1. Proposed Research Model
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responses were received with a response rate of 63.75 percent, which is a
standard response rate for yielding an accurate result (Uddin, Mahmood, &
Fan, 2019). The raw data was then entered into SPSS 20.0 data editor for
generating the required statistical analysis. We also employed Smart PLS2,
a second generation partial least square analytical tool used for structural
equation modeling to estimate the validity and reliability issues of the
measures in this study (Howladar, Rahman, & Uddin, 2018). We used
structural equation modeling via SmartPLS2 in place of simple regression
analysis because of the robustness and authenticity of the findings derived
through the integrated model (Hair, Hollingsworth, Randolph, & Chong,
2017).
4.2 Sample Characteristics
Table 1 exhibits the demographic profile of the respondents, including their
gender, age, academic qualifications, types of organization, the size of the
organization, and tenure experience. It reveals that workplaces are male-
dominated, with 63.4 percent men and 36.6 percent women. Additionally,
the age distribution of the respondents delineates that most of them (48.6
percent) were in the age range of 26-30 years, followed by 21-25 years (24.3
percent), 31-35 years (12.8 percent), 36-40 years (10.5 percent) and more
than 41 years (3.8 percent). The sample included respondents with different
educational qualifications, such as bachelors, masters, and others; where the
most significant number (73.2 percent) of respondents had a master degree.
Regarding organization type, we observed the almost an equal
representation of respondents from both the manufacturing and service
industries, 49.4 percent from manufacturing and 50.6 percent from service
sector, respectively. Finally, maximum responses (81.7 percent) were
received from large organizations. The average work experience of the
respondents was 3.63 years.
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Table 1.
Demographic Profile of Respondents (n=235)
Aspects Frequency Percentage (%)
Gender
Male
Female
149
86
63.4
36.6
Age
21-15
26-30
31-35
36-40
41 and above
57
114
30
25
9
24.3
48.5
12.8
10.5
3.8
Education
Bachelor
Master
Others
58
172
5
24.7
73.2
2.1
Type of Organization
Manufacturing
Service
116
119
49.4
50.6
Size of Organization
SME
Large
43
192
18.3
81.7
Working experience
(Mean)
3.63 years
4.3 Measurement Tools
The measurement tools used in his research were collected from prior
studies. Survey instruments of performance expectancy (Venkatesh et al.,
2003), effort expectancy (Venkatesh et al., 2003), social influence
(Venkatesh et al., 2003; Venkatesh et al., 2012), facilitating conditions
(Venkatesh et al., 2003; Venkatesh et al., 2012), resistance to change
(Laumer et al., 2016), perceived credibility (Wang, Wang, Lin, & Tang,
2003) behavioral intention (Venkatesh et al., 2012), and actual usage (Rajan
& Baral, 2015) were used. Some necessary amendments were made in
terms of face validity in the items for their better fit in the given context.
Measurement items are mentioned in appendix A1.
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5 Analysis and Discussion
5.1 Measurement Model Evaluation
The current study adopted structural equation modeling for applying
multiple regression analysis. Multiple regression via structural equation
modeling improves the authenticity of estimates by comprehensively
measuring the regression weights through the integration of the
measurement model and structured model analysis (Uddin et al., 2019).
Smart PLS is the most applied tool of structural equation modeling in
management sciences these days (Hair, Hult, Ringle, & Sarstedt, 2014). At
the measurement level, all items underlying a given construct were
examined to estimate their suitability. To do so, we investigated their cross-
loadings, reliability, convergent validity, and discriminant validity. Table 2
demonstrated the items’ cross-loading to their corresponding construct. It
exhibited that all the items loaded highly to their original construct than
other constructs, which revealed that all the items converged into their
constructs.
Table 2.
Cross-Loading of the Items
Items UB BI EE FC PC PE RC SI
pe1 0.218 0.662 0.512 0.439 0.537 0.902 0.200 0.537
pe2 0.351 0.638 0.564 0.480 0.514 0.907 0.243 0.550
pe3 0.275 0.556 0.521 0.426 0.497 0.896 0.232 0.546
pe4 0.280 0.612 0.532 0.485 0.500 0.894 0.288 0.517
ee1 0.332 0.612 0.917 0.419 0.434 0.576 0.268 0.480
ee2 0.330 0.565 0.919 0.403 0.387 0.484 0.241 0.451
ee3 0.423 0.640 0.937 0.418 0.451 0.573 0.274 0.483
ee4 0.396 0.638 0.918 0.470 0.481 0.545 0.242 0.506
si1 0.255 0.614 0.461 0.392 0.432 0.518 0.225 0.927
si2 0.309 0.623 0.477 0.441 0.445 0.540 0.279 0.900
si3 0.298 0.643 0.475 0.410 0.400 0.526 0.257 0.927
si4 0.324 0.562 0.504 0.480 0.469 0.511 0.211 0.926
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fc1 0.313 0.607 0.447 0.889 0.316 0.440 0.190 0.429
fc2 0.363 0.570 0.412 0.901 0.366 0.457 0.289 0.414
fc3 0.276 0.578 0.410 0.884 0.353 0.437 0.157 0.425
fc4 0.320 0.600 0.388 0.898 0.395 0.483 0.237 0.407
pc1 0.232 0.595 0.451 0.394 0.955 0.533 0.158 0.433
pc2 0.280 0.594 0.459 0.371 0.955 0.553 0.212 0.474
rc1 0.240 0.307 0.232 0.272 0.169 0.305 0.878 0.278
rc2 0.167 0.267 0.271 0.194 0.144 0.211 0.893 0.193
rc3 0.260 0.281 0.269 0.217 0.215 0.239 0.911 0.246
rc4 0.231 0.228 0.192 0.149 0.141 0.157 0.794 0.190
bi1 0.429 0.953 0.555 0.440 0.515 0.627 0.321 0.587
bi2 0.363 0.931 0.627 0.611 0.565 0.589 0.244 0.404
bi3 0.398 0.951 0.429 0.519 0.583 0.451 0.322 0.455
ub1 0.928 0.393 0.366 0.316 0.214 0.281 0.264 0.282
ub2 0.495 0.129 0.081 0.239 0.198 0.183 0.195 0.236
ub3 0.926 0.412 0.418 0.324 0.261 0.291 0.195 0.292
Note. UB= Use behavior, BI= Behavioral intention, EE= Effort expectancy,
FC= Facilitating condition, SI= Social influence, PC= Perceived credibility,
PE= Performance expectancy, and RC= Resistance to change.
Table 3 reported that the minimum composite reliability of any
construct is 0.842 (actual usage), which is above the minimum threshold
limit (≥0.70) (Hair, Black, Babin, & Anderson, 2014). Convergent validity
was examined through scrutinizing the average variance extracted. Table 3
delineated that minimum average variance extracted in this study is 0.655,
which is also above the minimum cut off value (≥0.50) (Hair, Hult, Ringle,
& Sarstedt, 2016). Discriminant validity is shown through the diagonal
italic-bold value in the correlation matrix. It reflects a very good fit because
the square root of an average variance extracted of any construct is higher
than its correlation with other constructs (Mahmood, Uddin, & Luo, 2019).
Thus, validity and reliability were accurately ensured.
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Table 3.
Convergent and Discriminant Validities
Note: AVE= Average variance extracted, CR= Composite reliability, UB= Use behavior,
BI= Behavioral intention, EE= Effort expectancy, FC= Facilitating condition, SI= Social
influence, PC= Perceived credibility, PE= Performance expectancy, and RC= Resistance
to change.
5.2 Structural Model Evaluation
In structural equation modeling, as depicted in figure 2, we considered
several issues to maintain the heightened standard. In this section, we
considered the beta-coefficient (β), coefficient of determination (R2), and
goodness of fit index (GoF). β-coefficient exhibited the strength of the effect
of an exogenous variable on the endogenous variable and R2 underlined the
overall predictive power of the structured model. The bootstrapping results
of sample cases of 5000 showed that only one path has insignificant
estimates. We witnessed that the observed variables explain 76.3 percent
change in the intention to use, and the intention to use also accounted for
17.7% change (R2) in actual usage behavior. Following the tenets of Cohen
(1988), the minimum R2 in these two exogenous variables was achieved. In
line with the conceptualization of Tenenhaus, Vinzi, Chatelin, and Lauro
(2005), we were inclined to calculate GoF, which is the square root of the
products of average variance extracted, and R2. The calculated effect size is
significant (Cohen, 1977) because the calculated GoF, in equation (i),
showed an excellent effect size (0.619) with a minimum average variance
extracted (≥0.50) (Azim, Fan, Uddin, Kader, Jilani, & Begum, 2019;
Fornell & Larcker, 1981; Yi, Uddin, Das, Mahmood, & Sohel, 2019).
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Figure 2. Structural Model with Path Coefficients
𝐺𝑜𝐹 = √(𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑉𝐸 ∗ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅2) ---------Equation (i)
𝐺𝑜𝐹 = √0.815 ∗ 0.470
𝐺𝑜𝐹 = 0.619
5.3 Hypotheses Testing
The following table 4 exhibits the results of the hypotheses along with their
relevant estimates. In H1, it was proposed that the perceived effort predicted
the behavioral intention. The result demonstrates that the effect (PEBI) is
significant (β=0.270, t-value=2.458, p.value=0.015), and the hypothesis is
supported. H2 hypothesized that effort expectancy has a significant effect
on behavioral intention. Estimates show that the effect (EEBI) is not
significant (β=0.181, t-value=1.715, p.value=0.088). Hence, the
hypothesis is not supported. We hypothesized in H3 that social influence is
a predictor of behavioral intention. The result shows that its influence on
behavioral intention (SIBI) is significant (β=0.227, t-value=2.003,
p.value=0.046). Hence the hypothesis (H3) is supported. In H4, it was
proposed that facilitating conditions predicted behavioral intention. The
result shows that the effect (FCBI) is significant (β=0.257, t-
value=2.829, p.value=0.005). Thus, this hypothesis is supported. H5
hypothesized that resistance to change has a significant effect on behavioral
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intention. The result demonstrates that the effect (RCBI) is not significant
(β=0.037, t-value 0.724, p.value=0.470). Henceforth this hypothesis is not
supported. We hypothesized in H6 that perceived credibility is a strong
predictor of behavioral intention. The result shows that its influence on
behavioral intention (PCBI) is significant (β=0.165, t-value=2.230,
p.value=0.027). Thus the hypothesis is accepted. Finally, we also
hypothesized in H7 that behavioral intention is a predictor of actual usage.
The result shows that it has a strong influence on actual usage (BIUB)
which is significant (β=0.421, t-value=4.981, p.value=0.000). Eventually,
it can also be concluded that H7 is also supported.
Table 4
Estimates on the Path Coefficient
Note: UB= Use behavior, BI= Behavioral intention, EE= Effort expectancy, FC=
Facilitating condition, SI= Social influence, PC= Perceived credibility, PE= Performance
expectancy, and RC= Resistance to change.
6. Discussion
This study tested an extended UTAUT model to determine the users’
behavioral intention to adopt and implement ERP. The core objective of this
study was to reveal the influencing factors, which determine attitude
towards adoption of ERP and its successful implementation in
organizations. Five out of seven hypotheses were supported in this study.
Consistent with the findings of Venkatesh et al. (2003), Martins, Oliveira,
and Popovič (2014), Casey and Wilson (2012) and Escobar and Carvajal
(2014), it was found that performance expectancy has a positive influence
on behavioral intention. It denotes that performance expectancy of using the
ERP system significantly explains the end users’ behavioral intention about
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it. Moreover, when users believe in the performance of ERP, their positive
intention toward ERP enhances.
Also, it was found that facilitating condition positively influence
behavioral intention, which reflects that facilitating conditions predict the
adoption of ERP. It also states that adequate resource in the custody of any
firm promotes the intention to adopt an ERP system. The result is found
consistent with the findings of Salloum and Shaalan (2018) and Suki (2017),
who assert that the availability of organizational and technical support along
with individual capability leads to the intention to use a given technology.
In line with the findings of Venkatesh et al. (2003) and Escobar and Carvajal
(2014), this study found a positive impact of social influence on behavioral
intention. The consistent findings with prior studies strengthen the
generalizability of the current results to the fact that end users’ adoption of
ERP is significantly shaped by the society they belong to.
Surprisingly, the current study revealed that effort expectancy and
resistance to change are not significantly associated with the behavioral
intention to use ERP. Effort expectancy measures the degree to which a
person believes the system is easy to use. Technology phobia in the studied
context made the effect of it on the intention to use ERP insignificant
(Salloum & Shaalan, 2018). The inconsistent findings of the influence of
resistance to change on behavioral intention are not surprising since there
are many cultural peculiarities (Hofstede, 2001). Since the people believe in
a hierarchical society, users in Bangladesh are greatly influenced by the
people who are close and also vital to them. They intend to use ERP for their
day to day activity because of the people around them. Likewise, people in
collective society tend to avoid risk and resist any change (Hofstede, 2001).
Finally, the impact of behavioral intention on usage behavior was also
found to be significant. The result is also found consistent with the findings
of Venkatesh et al. (2003), Escobar and Carvajal (2014), Yu (2012), and
Ifinedo (2012). This is a clear indication of the continuous usage of ERP in
organizations of Bangladesh. Furthermore, perceived credibility was found
a significant predictor of behavioral intention to use ERP. This result is
found consistent with the findings of Dasgupta et al.(2011), and Jeong and
Yoon (2013). This means that even though Bangladeshi organization and
individual users have become more technically savvy and more technology
ignorant, but they still value the contribution of ERP adoption.
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6.1 Contributions of Study
We observed that various studies on the adoption of an information system
and ERP using the UTAUT model were conducted mostly in the advanced
countries. Our literature and survey posited that the rate of adoption and
implementation of ERP in a developed country is much higher than the
developing countries. Notably, the application of an ERP system is a million
dollar investment and the failure to implement makes a firm financially
vulnerable. Thus, we do not observe a significant amount of studies in the
context of South Asia as well as Bangladesh. This was the primary reason
to investigate the adoption and implementation of ERP. Studies showed that
ERP transforms today’s business and enhances organizations’
competitiveness. If Bangladesh or any other developing country wants to
turn itself into the business process reengineering, the business
organizations have to adopt and implement ERP successfully. So, this study
will help the policy makers to understand the significance of using ERP to
excel in key industries. Additionally, Venkatesh et al. (2012), Venkatesh et
al. (2011), and Rajan and Baral (2015) attested to further study UTAUT
adoption and implementation in various contexts to validate the
generalizations of this model. Consistent with this, the current findings in
Bangladeshi context, which is an emerging country in South Asia, will
advance and generalize the understanding of UTAUT applications in a
different context.
Till date, the existing studies have observed a few areas that warrant
further studies to contribute by filling the vacuum in the latest literature.
Firstly, studies showed that considerable research has been conducted
globally, as most of the reviews were deemed to have a western bias (Huang
& Palvia, 2001). However, little is known about developing and the least
developed countries which prevents the generalizability of findings (AlBar
& Hoque, 2019). Thus further studies in various contexts are needed to draw
the inference on the causality of the result. Secondly, few researchers
focused on challenges impeding ERP adoption and implementation
(Fernandez, Zaino, & Ahmad, 2018). Interestingly, they failed to unearth
the factors behind ERP adoption and implementation while taking them
both together. Finally, Awa (2018) and Nandi and Vakkayil (2018) reported
that prior studies over-emphasized the technical aspects to adopt and
implement ERP. Unfortunately, the behavioral perspective of end users’
adoption and implementation of ERP was ignored. Henceforth, the current
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study on the manufacturing and service firms in Bangladesh is going to
contribute to the existing literature by addressing the issues above based on
the tenet of UTAUT.
6.2 Research Implications
This study extended the existing UTAUT model with distinct constructs,
such as perceived credibility and resistance to change to identify the factors
leading to the adoption and implementation of ERP in Bangladesh and also
to identify the degree of influence of each element. It also advanced an
extensive review of the literature and a field survey about the adoption and
implementation of ERP in the context of a developing country. Although
the current study found that the impact of effort expectancy and resistance
to change is not significantly evident. Despite the fact that the research
shows inconsistent results with prior studies; it will surely predict an almost
similar story of other developing countries in South Asia like Bangladesh,
regarding information system usage, attitude towards adopting technology
and compatibility of socio-economic status concerning the factors (Ram,
Corkindale, & Wu, 2014). More importantly, the current research also
advances the knowledge and literature since we conducted it in a context
which is primarily ignored (Huang & Palvia, 2001). A study in an Asian
country will feed and substantiate the generalizability of the previous
findings. Furthermore, prior research focused on the technical aspect of ERP
adoption (Rajan & Baral, 2015) and complexities in it (Fernandez et al.,
2018). On the contrary, a study on the behavioral adoption and
implementation of ERP was needed. Therefore, the survey on the adoption
and implementation of it will contribute to a large extent by filling that
knowledge gap.
The findings of the study contribute to the existing body of research by
informing the essence of ERP to maximize its widespread adoption both in
small and medium enterprises and large organizations in Bangladesh. This
study encloses valuable insights for ERP vendors, the information
technology planning agency, practicing managers, and policy makers to
identify an opportunity for market expansion, and to develop strategies for
successful adoption, implementation and acceleration of ERP technology
among end users (Hasan et al., 2018; Reitsma & Hilletofth, 2018). Most
importantly, it will undoubtedly help the potential ERP users to build a solid
technologically enabled base for accelerating economic growth and
achieving the digital Bangladesh goal by making substantial investments in
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information technology infrastructure. Additionally, the findings are more
insightful for ERP implementation in small and medium enterprises, since
prior studies showed that ERP project experiences more failure than larger
enterprises (Zach, Munkvold, & Olsen, 2014).
6.3 Policy Implications
In line with the study’s findings, policy makers and practicing professionals
of ERP will be facilitated and may take an active role while adopting and
implementing ERP. Particularly, the results of the study will facilitate the
adoption and implementation of ERP among the business owners and new
entrepreneurs in Bangladesh. It is also noteworthy that the current findings
will assist the ERP vendor in communicating the essential factors of ERP
adoption and implementation to the end users in the context of Bangladesh.
Henceforth, the results will feed them to customize the future system in
order to realize the fullest market potential. Unlike prior research, one of the
most important notes for the ERP vendor in Bangladesh is to focus less on
effort expectancy and resistance to change from the end users’ side. A
critical emphasis from ERP vendors on trust, performance, affordability,
and social approval will pay back a considerable return to the corporate
bottom line. Besides, the current findings will also assist the government
and other funding agencies to come forward and use estimates for designing
policy guidelines for the broader applications of ERP.
7. Limitations and Direction for Future Research
The current study has some limitations. The sample data was collected from
various firms representing both the service and manufacturing industries,
which lacks a comprehensive and exhaustive industry wide panorama.
Surprisingly, most of the replies were received from large organizations,
which limit the extrapolation of the findings irrespective of organizational
size. ERP is still limitedly applied to the studied arena, and a considerable
portion of the respondents did not have a good idea of the research topic.
Additionally, the sample was obtained from just one south Asian country
and it represented a nationwide perspective that made the results context
based, preventing causal inference (Awa, 2018). Although the results are
statistically relevant, further surveys with a broader territorial scope and
greater sample size will increase the model’s analytical capabilities for the
generalizability of the finding (Awa, 2018; Fan, Mahmood, & Uddin,
2019).
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Journal of Management and Research (JMR) Volume 6(1): 2019
Since we have calculated the result by using cross-sectional data, it
prevents the generalizability of the findings. Henceforth, we recommend
future researchers to either execute action research or use longitudinal data
or to adopt the mixed method of study for limiting the chances of being
particularized (Qi Dong, 2009; Ram et al., 2014). Drawing on the
integrationist perspective, the implementation of any technological
innovation must go abreast of multiple influencing factors (Mahmood et al.,
2019). Interestingly, this study posited some direct effects of the exogenous
variables on their aspired endogenous variables. Thus, taking confounding
variables namely moderators and mediators into consideration while
generalizing the findings may ensure the robustness of the results and
displays an accurate glimpse of the underlying observations (Qi Dong,
2009).
8. Conclusion
In this study, a conceptual model was developed and a survey instrument
was constructed to gather data for testing hypothesized model relationships.
We examined factors such as performance expectancy, effort expectancy,
social influence, facilitating condition, perceived credibility, and resistance
to change, which were influencing the adoption and implementation of ERP
in the service and manufacturing firms in Bangladesh. All these factors
except resistance to change and effort expectancy are significantly
associated with adoption and implementation of an ERP system in
Bangladesh. The reported results advance the previously held knowledge
by providing a more in-depth insight about ERP adoption and
implementation through testing an extended UTAUT model in a dissimilar
context. This study provides an in-depth understanding of the factors
influencing ERP adoption and implementation among academicians, policy
makers, and industrial practitioners.
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Appendix A1.
Measurement tools Statements
Performance expectancy Using ERP improves my productivity
I would find ERP useful in my job
Using ERP enables me to accomplish tasks more quickly
If I use ERP, I will increase my chances of
getting a raise
Effort expectancy Learning to operate ERP is easy for me
I would find that ERP is easy for me to use
It would be easy for me to become skillful in
using ERP My job related activities with ERP are clear and
understandable
Social influence People who are important to me think that I should use ERP
People who affect/influence my behavior think
that I should use ERP
People whose opinions I value prefer that I must use ERP
In general, the organization has supported the
use of ERP
Facilitating conditions I have the resources necessary to use ERP
I have the knowledge necessary to use ERP
ERP is not compatible with other available
software/technologies I use I can get help from others if I have difficulties
using ERP
Behavioral Intention I intend to continue using ERP in the future
I will always try to use ERP in my daily life
I plan to continue to use ERP frequently
Resistance to change I will not comply with the change to the new
way of working with ERP
I will not cooperate with the change to the new
way of working with ERP
I oppose the change to the new way of working with ERP
I do not agree with the change to the new way of
working with ERP
Actual usage I have been using ERP for the last few weeks
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I am using ERP regularly
I am giving a lot of time to ERP applications
Perceived credibility Using ERP would not divulge my personal
information
I would find ERP secure in conducting
organizational tasks
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References
Addo, R. T., & Helo, P. (2011). Enterprise resource planning (ERP): A
review literature report. In Proceeding of the World Congress on
Engineering and Computer Science, 2, 19-21.
Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology
acceptance model in hospitals. International Journal of Medical
Informatics, 78(2), 115–126.
AlBar, A. M., & Hoque, M. R. (2019). Factors affecting cloud ERP
adoption in Saudi Arabia: An empirical study. Information
Development, 35(1), 150–164.
Ali, M., & Miller, L. (2017). ERP system implementation in large
enterprises–A systematic literature review. Journal of Enterprise
Information Management, 30(4), 666–692.
Asamoah, D., & Andoh, F. K. B. (2018). Antecedents and outcomes of
extent of erp systems implementation in the Sub-Saharan Africa
context: A panoptic perspective. Communications of the Association for
Information Systems, 42(1), 581–601.
Audia, P. G., & Brion, S. (2007). Reluctant to change: Self-enhancing
responses to diverging performance measures. Organizational
Behavior and Human Decision Processes, 102(2), 255–269.
Awa, H. O. (2018). Some antecedent factors that shape actors’ adoption of
enterprise systems. Enterprise Information Systems, 13(1), 1–25.
Awa, H. O., Uko, J. P., & Ukoha, O. (2017). An empirical study of some
critical adoption factors of ERP software. International Journal of
Human–Computer Interaction, 33(8), 609–622.
Azim, M. T., Fan, L., Uddin, M. A., Kader, A., Jilani, M. M., & Begum, S.
(2019). Linking transformational leadership with employees’
engagement in the creative process. Management Research Review,
42(7), 837-858.
Bhatiasevi, V. (2016). An extended UTAUT model to explain the adoption
of mobile banking. Information Development, 32(4), 799–814.
Bhattacherjee, A., & Hikmet, N. (2008). Reconceptualizing organizational
support and its effect on information technology usage: Evidence from
the health care sector. Journal of Computer Information Systems, 48(4),
69–76.
Adoption and Implementation of ERP | 143
Journal of Management and Research (JMR) Volume 6(1): 2019
Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn,
B. (2012). Factors influencing the Thai elderly intention to use
smartphone for e-Health services. Paper presented at the IEEE
Symposium on Humanities, Science and Engineering Research
(SHUSER), Kuala Lumpur, Malaysia.
Calisir, F., Gumussoy, C. A., & Bayram, A. (2009). Predicting the
behavioral intention to use enterprise resource planning systems: An
exploratory extension of the technology acceptance model.
Management Research News, 32(7), 597–613.
Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., & Walden, P.
(2006). Adoption of mobile devices/services—Searching for answers
with the UTAUT. Paper presented at the 39th Annual Hawaii
International Conference on System Sciences, Kauai, HI, USA.
Casey, T., & Wilson, E. E. (2012). Predicting uptake of technology
innovations in online family dispute resolution services: An application
and extension of the UTAUT. Computers in Human Behavior, 28(6),
2034–2045.
Chang, M.-K., Cheung, W., Cheng, C. H., & Yeung, J. H. Y. (2008).
Understanding ERP system adoption from the user's perspective.
International Journal of Production Economics, 113(2), 928–942.
Cohen, J. (1977). Statistical power analysis for the behavioral sciences.
New York, NY: Academic Press.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences.
Hillsdale, NJ: Lawrence Erlbaum.
Costa, C. J., Ferreira, E., Bento, F., & Aparicio, M. (2016). Enterprise
resource planning adoption and satisfaction determinants. Computers in
Human Behavior, 63, 659–671.
Darr, A. (2015). Convergence of service and technical skills: The case of
ERP implementation in Israel. Asian Journal of Technology Innovation,
23(S), 26–39.
Dasgupta, S., Paul, R., & Fuloria, S. (2011). Factors affecting behavioral
intentions towards mobile banking usage: empirical evidence from
India. Romanian Journal of Marketing, 1, 6–28.
Davenport, T. H. (1998). Putting the enterprise into the enterprise system.
Harvard Business Review, 76(4), 121–131.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user
acceptance of information technology. MIS Quarterly, 13(3), 319–340.
144 | Adoption and Implementation of ERP
Journal of Management and Research (JMR) Volume 6(1): 2019
Doom, C., Milis, K., Poelmans, S., & Bloemen, E. (2010). Critical success
factors for ERP implementations in Belgian SMEs. Journal of
Enterprise Information Management, 23(3), 378–406.
Escobar, T. R., & Carvajal, E. T. (2014). Online purchasing tickets for low
cost carriers: an application of the unified theory of acceptance and use
of technology (UTAUT) model. Tourism Management, 43, 70–88.
Esteves, J., & Bohorquez, V. (2007). An updated ERP systems annotated
bibliography: 2001-2005. Communications of the Association for
Information System, 19(18), 386-346.
Everdingen, Y. V., Hillegersberg, J. V., & Waarts, E. (2000). Enterprise
resource planning: ERP adoption by European midsize companies.
Communications of the ACM, 43(4), 27–31.
Fan, L., Mahmood, M., & Uddin, M. A. (2019). Supportive chinese
supervisor, innovative international students: A social exchange theory
perspective. Asia Pacific Education Review, 20(1), 101–115.
Fernandez, D., Zaino, Z., & Ahmad, H. (2018). An investigation of
challenges in enterprise resource planning (ERP) implementation: The
case of public sector in Malaysia. International Journal of Supply Chain
Management, 7(3), 113–117.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models
with unobservable variables and measurement error. Journal of
Marketing Research, 18(1), 39–50.
Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of
elderly acceptance of preventive mobile health services in China.
Electronic Markets, 23(1), 49–61.
Gupta, S., Misra, S. C., Kock, N., & Roubaud, D. (2018). Organizational,
technological and extrinsic factors in the implementation of cloud ERP
in SMEs. Journal of Organizational Change Management, 31(1), 83–
102.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017).
An updated and expanded assessment of PLS-SEM in information
systems research. Industrial Management & Data Systems, 117(3),
442–458.
Hair, J. F. Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014).
Multivariate data analysis: A global perspective (7th ed.). London:
Pearson.
Adoption and Implementation of ERP | 145
Journal of Management and Research (JMR) Volume 6(1): 2019
Hair, J. F. Jr., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2014). A primer
on partial least squares structural equation modeling (PLS-SEM).
London: SAGE Publications.
Hair, J. F. Jr., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer
on partial least squares structural equation modeling (PLS-SEM).
London: Sage Publications.
Hasan, M. S., Ebrahim, Z., Mahmood, W. H. W., & Rahmanm, M. N. A.
(2018). Factors influencing enterprise resource planning system: A
review. Journal of Advanced Manufacturing Technology, 12(1), 247–
258.
Hofstede, G. (2001). Culture's consequences: Comparing values,
behaviors, institutions and organizations across nations. London: Sage
Publications.
Howladar, M. H. R., Rahman, M. S., & Uddin, M. A. (2018). Deviant
workplace behavior and job performance: The moderating effect of
transformational leadership. Iranian Journal of Management Studies,
11(1), 147–183.
Huang, Z., & Palvia, P. (2001). ERP implementation issues in advanced and
developing countries. Business Process Management Journal, 7(3),
276–284.
Huy, Q. N. (1999). Emotional capability, emotional intelligence, and radical
change. Academy of Management Review, 24(2), 325–345.
Ifinedo, P. (2012, January 4-7). Technology acceptance by health
professionals in Canada: An analysis with a modified UTAUT model.
Paper presented at the Hawaii International Conference on System
Sciences, Washington, DC, USA.
Jeong, B. K., & Yoon, T. E. (2013). An empirical investigation on consumer
acceptance of mobile banking services. Business and Management
Research, 2(1), 31–40.
Khanam, L., Uddin, M. A., & Mahfuz, M. A. (2015). Students’ behavioral
intention and acceptance of e-recruitment system: A Bangladesh
perspective. Paper presented at the The 12th International Conference
on Innovation and Management, ICIM2015, Wuhan, China.
Klaus, H., Rosemann, M., & Gable, G. G. (2000). What is ERP?
Information Systems Frontiers, 2(2), 141–162.
146 | Adoption and Implementation of ERP
Journal of Management and Research (JMR) Volume 6(1): 2019
Krotov, V., Boukhonine, S., & Ives, B. (2011). ERP implementation gone
terribly wrong: The case of natural springs. Communications of the
Association for Information Systems, 28, 277–282.
Kwahk, K. Y., & Kim, H. W. (2008). Managing readiness in enterprise
systems-driven organizational change. Behaviour & Information
Technology, 27(1), 79–87.
Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain
management. Industrial Marketing Management, 29(1), 65–83.
Laumer, S., Maier, C., Eckhardt, A., & Weitzel, T. (2016). User personality
and resistance to mandatory information systems in organizations: A
theoretical model and empirical test of dispositional resistance to
change. Journal of Information Technology, 31(1), 67–82.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use
information technology? A critical review of the technology acceptance
model. Information & Management, 40(3), 191–204.
Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social
influences and adoption of wireless internet services via mobile
technology. The Journal of Strategic Information Systems, 14(3), 245–
268.
MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in
marketing: Causes, mechanisms, and procedural remedies. Journal of
Retailing, 88(4), 542–555.
Mahmood, M., Uddin, M. A., & Luo, F. (2019). Influence of
transformational leadership on employees’ creative process
engagement: A multi-level analysis. Management Decision, 57(3),
741–764.
Markus, M. L., & Tanis, C. (Eds.). (2000). The enterprise systems
experience - from adoption to success. Cincinnati, OH: Pinnaflex
Educational Resources.
Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet
banking adoption: A unified theory of acceptance and use of technology
and perceived risk application. International Journal of Information
Management, 34(1), 1–13.
McAfee, A. (2009). Enterprise 2.0: New collaborative tools for your
organization's toughest challenges. Harvard: Harvard Business Press.
Meyer, B. (1988). Object-oriented software construction. New York:
Prentice Hall.
Adoption and Implementation of ERP | 147
Journal of Management and Research (JMR) Volume 6(1): 2019
Moon, Y. B. (2007). Enterprise resource planning (ERP): A review of the
literature. International Journal of Management and Enterprise
Development, 4(3), 235–264.
Nandi, M. L., & Vakkayil, J. (2018). Absorptive capacity and ERP
assimilation: The influence of company ownership. Business Process
Management Journal, 24(3), 695–715.
ORACLE. (2018). What is ERP? Retrieved from
https://www.oracle.com/applications/erp/what-is-erp.html
Pang, A. (2017). Top 10 ERP software vendors and market forecast 2016-
2021. Retrieved November 14, 2018, from
https://www.appsruntheworld.com/top-10-erp-software-vendors-and-
market-forecast/
Panigrahi, S., Zainuddin, Y., & Azizan, N. (2014). Investigating key
determinants for the success of knowledge management system (KMS)
in higher learning institutions of Malaysia using structural equation
modeling. The International Journal of Humanities & Social Studies,
2(6), 202–209.
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information
systems success: Models, dimensions, measures, and interrelationships.
European Journal of Information Systems, 17(3), 236–263.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of
method bias in social science research and recommendations on how to
control it. Annual Review of Psychology, 63, 539–569.
Porter, M. E. (1991). Towards a dynamic theory of strategy. Strategic
Management Journal, 12(S2), 95–117.
Qi Dong, J. (2009). User acceptance of information technology innovations
in the Chinese cultural context. Asian Journal of Technology
Innovation, 17(2), 129–149.
Rajan, C. A., & Baral, R. (2015). Adoption of ERP system: An empirical
study of factors influencing the usage of ERP and its impact on end user.
IIMB Management Review, 27(2), 105–117.
Ram, J., Corkindale, D., & Wu, M.-L. (2014). ERP adoption and the value
creation: Examining the contributions of antecedents. Journal of
Engineering and Technology Management, 33, 113–133.
Reitsma, E., & Hilletofth, P. (2018). Critical success factors for ERP system
implementation: A user perspective. European Business Review, 30(3),
285–310.
148 | Adoption and Implementation of ERP
Journal of Management and Research (JMR) Volume 6(1): 2019
Rouhani, S., & Mehri, M. (2018). Empowering benefits of ERP systems
implementation: Empirical study of industrial firms. Journal of Systems
and Information Technology, 20(1), 54–72.
Saatçıoğlu, Ö. Y. (2009). What determines user satisfaction in ERP projects:
Benefits, barriers or risks? Journal of Enterprise Information
Management, 22(6), 690–708.
Salloum, S. A., & Shaalan, K. (2018). Factors affecting students’
acceptance of e-learning system in higher education using utaut and
structural equation modeling approaches. Paper presented at the
International Conference on Advanced Intelligent Systems and
Informatics, Cairo, Egypt.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of
reasoned action: A meta-analysis of past research with
recommendations for modifications and future research. Journal of
Consumer Research, 15(3), 325–343.
Singh, N. P. (2007). ERP implementation based on ASP model. Global
Business Review, 8(1), 29–52.
Smither, J. A. A., & Braun, C. C. (1994). Technology and older adults:
Factors affecting the adoption of automatic teller machines. The Journal
of General Psychology, 121(4), 381–389.
Star, T. D. (2018). Active internet connections 9cr. Retrieved from
https://www.thedailystar.net/business/internet-users-bangladesh-over-
9-core-active-1636477
Sternad, S., & Bobek, S. (2013). Impacts of TAM-based external factors on
ERP acceptance. Procedia Technology, 9, 33–42.
Suki, N. M. (2017). Determining students’ behavioural intention to use
animation and storytelling applying the UTAUT model: The
moderating roles of gender and experience level. The International
Journal of Management Education, 15(3), 528–538.
Sun, Y., Bhattacherjee, A., & Ma, Q. (2009). Extending technology usage
to work settings: The role of perceived work compatibility in ERP
implementation. Information & Management, 46(6), 351–356.
Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path
modeling. Computational Statistics & Data Analysis, 48(1), 159–205.
Uddin, M. A., Mahmood, M., & Fan, L. (2019). Why individual employee
engagement matters for team performance? mediating effects of
Adoption and Implementation of ERP | 149
Journal of Management and Research (JMR) Volume 6(1): 2019
employee commitment and organizational citizenship behaviour. Team
Performance Management: An International Journal, 25(1/2), 47–68.
United Nations Organization. (2016). Sustainable development goals
(SDGs). Retrieved from http://www.un-bd.org/Plain/Post2015
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the
technology acceptance model: Four longitudinal field studies.
Management Science, 46(2), 186–204.
Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for
directions? gender, social influence, and their role in technology
acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User
acceptance of information technology: Toward a unified view. MIS
Quarterly, 27(3), 425–478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and
use of information technology: Extending the unified theory of
acceptance and use of technology. MIS Quarterly, 36(1), 157–178.
Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S.
A. (2011). Extending the two-stage information systems continuance
model: Incorporating UTAUT predictors and the role of context.
Information Systems Journal, 21(6), 527–555.
Wagaw, M. (2017). Acceptance of homegrown enterprise resource
planning (ERP) systems in Ethiopia. Applied Informatics, 4(1), 6.
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants
of user acceptance of internet banking: an empirical study. International
Journal of Service Industry Management, 14(5), 501–519.
Yagci, M. I., Biswas, A., & Dutta, S. (2009). Effects of comparative
advertising format on consumer responses: the moderating effects of
brand image and attribute relevance. Journal of Business Research,
62(8), 768–774.
Yi, L., Uddin, M. A., Das, A. K., Mahmood, M., & Sohel, S. M. (2019). Do
transformational leaders engage employees in sustainable innovative
work behaviour? perspective from a developing country. Sustainability,
11(9), 2485.
Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding
information technology acceptance by individual professionals:
Toward an integrative view. Information & Management, 43(3), 350–
363.
150 | Adoption and Implementation of ERP
Journal of Management and Research (JMR) Volume 6(1): 2019
Youngberg, E., Olsen, D., & Hauser, K. (2009). Determinants of
professionally autonomous end user acceptance in an enterprise
resource planning system environment. International Journal of
Information Management, 29(2), 138–144.
Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking:
Empirical evidence from the UTAUT model. Journal of Electronic
Commerce Research, 13(2), 104–123.
Zach, O., Munkvold, B. E., & Olsen, D. H. (2014). ERP system
implementation in SMEs: Exploring the influences of the SME context.
Enterprise Information Systems, 8(2), 309–335.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business
research method (8th ed.). South-Western, Canada: Cengage Learning.
.